001     1022048
005     20240403082805.0
024 7 _ |a 10.1109/IGARSS52108.2023.10281579
|2 doi
024 7 _ |a WOS:001098971607156
|2 WOS
037 _ _ |a FZJ-2024-01185
041 _ _ |a English
100 1 _ |a Pato, Miguel
|0 P:(DE-HGF)0
|b 0
111 2 _ |a IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
|c Pasadena
|d 2023-07-16 - 2023-07-21
|w CA
245 _ _ |a Fast Machine Learning Simulator of At-Sensor Radiances for Solar-Induced Fluorescence Retrieval with DESIS and Hyplant
260 _ _ |c 2023
|b IEEE
300 _ _ |a 7563-7566
336 7 _ |a CONFERENCE_PAPER
|2 ORCID
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a Output Types/Conference Paper
|2 DataCite
336 7 _ |a Contribution to a conference proceedings
|b contrib
|m contrib
|0 PUB:(DE-HGF)8
|s 1706625116_2473
|2 PUB:(DE-HGF)
520 _ _ |a In many remote sensing applications the measured radi-ance needs to be corrected for atmospheric effects to studysurface properties such as reflectance, temperature or emis-sion features. The correction often applies radiative transferto simulate atmospheric propagation, a time-consuming stepusually done offline. In principle, an efficient machine learn-ing (ML) model can accelerate the simulation step. This is thegoal pursued here in the context of solar-induced fluorescence(SIF) emitted by vegetation around the O2-A band using thespaceborne DESIS and airborne HyPlant spectrometers. Wepresent an ML simulator of at-sensor radiances trained onsynthetic spectra and describe its performance in detail. Thesimulator is fast and accurate, constituting a promising alter-native to a full-fledged, lengthy radiative transfer code for SIFretrieval in the O2-A band with DESIS and HyPlant.Index Terms— solar-induced fluorescence, hyperspectralsensors, radiative transfer, machine learning
536 _ _ |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5112
|c POF4-511
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef Conference
700 1 _ |a Alonso, Kevin
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Auer, Stefan
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Buffat, Jim
|0 P:(DE-Juel1)188104
|b 3
|u fzj
700 1 _ |a Carmona, Emiliano
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Maier, Stefan
|0 P:(DE-Juel1)188300
|b 5
|u fzj
700 1 _ |a Müller, Rupert
|0 P:(DE-HGF)0
|b 6
700 1 _ |a Rademske, Patrick
|0 P:(DE-Juel1)162306
|b 7
|u fzj
700 1 _ |a Rascher, Uwe
|0 P:(DE-Juel1)129388
|b 8
|u fzj
700 1 _ |a Scharr, Hanno
|0 P:(DE-Juel1)129394
|b 9
|u fzj
773 _ _ |a 10.1109/IGARSS52108.2023.10281579
856 4 _ |u https://juser.fz-juelich.de/record/1022048/files/Fast_Machine_Learning_Simulator_of_At-Sensor_Radiances_for_Solar-Induced_Fluorescence_Retrieval_with_DESIS_and_Hyplant.pdf
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/1022048/files/Fast_Machine_Learning_Simulator_of_At-Sensor_Radiances_for_Solar-Induced_Fluorescence_Retrieval_with_DESIS_and_Hyplant.gif?subformat=icon
|x icon
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/1022048/files/Fast_Machine_Learning_Simulator_of_At-Sensor_Radiances_for_Solar-Induced_Fluorescence_Retrieval_with_DESIS_and_Hyplant.jpg?subformat=icon-1440
|x icon-1440
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/1022048/files/Fast_Machine_Learning_Simulator_of_At-Sensor_Radiances_for_Solar-Induced_Fluorescence_Retrieval_with_DESIS_and_Hyplant.jpg?subformat=icon-180
|x icon-180
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/1022048/files/Fast_Machine_Learning_Simulator_of_At-Sensor_Radiances_for_Solar-Induced_Fluorescence_Retrieval_with_DESIS_and_Hyplant.jpg?subformat=icon-640
|x icon-640
|y Restricted
909 C O |o oai:juser.fz-juelich.de:1022048
|p VDB
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)188104
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)188300
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 7
|6 P:(DE-Juel1)162306
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 8
|6 P:(DE-Juel1)129388
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 9
|6 P:(DE-Juel1)129394
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5112
|x 0
914 1 _ |y 2023
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IAS-8-20210421
|k IAS-8
|l Datenanalyse und Maschinenlernen
|x 0
980 _ _ |a contrib
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)IAS-8-20210421
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21